#PRODUCT STRATEGY #AI PLATFORM #REVENUE PROTECTION

Turning a $26M compliance platform into an AI-native product

C&R had $26M ARR in a legacy compliance platform and customers were leaving — the effort to maintain it was too high. I was brought in to define and lead the AI-first replacement, starting with validating demand before committing to the build.

COMPANY
Compliance & Risks

ROLE
Senior Manager, Product Experience & Market Insights

TIMELINE
2024–2026

THE SITUATION

A $26M legacy platform losing customers to effort, not competitors

C&R had a $26M ARR legacy compliance platform — 393 customers, built around expert-led manual workflows. Customers were leaving, not because a competitor was better, but because the product demanded too much effort to maintain. $3.76M was explicitly at risk, another $9.3M sat in a sensitivity band.

The decision to build an AI-first automated replacement had been made before I joined. I was brought in to define and lead that product — in close partnership with the PM — with a focus on customer discovery, rapid definition, and getting something real in front of users as fast as possible.

WHAT WE BUILT AND HOW

Choosing Sustainability as V1

C&R had a $26M ARR legacy compliance platform — 393 customers, built around expert-led manual workflows. Customers were leaving, not because a competitor was better, but because the product demanded too much effort to maintain. $3.76M was explicitly at risk, another $9.3M sat in a sensitivity band.

The decision to build an AI-first automated replacement had been made before I joined. I was brought in to define and lead that product — in close partnership with the PM — with a focus on customer discovery, rapid definition, and getting something real in front of users as fast as possible.

Defining Product Compliance

With V1 validated, I led the definition of the next product: full Product Compliance. Same approach — rapid discovery, close customer liaison, definition before engineering commitment, then design. The shift in product framing that came out of that work:

From: Assess each regulation manually
To: Can I sell this product in this market, and what do I need to do?

The sequencing decision that unlocked trust

The first version shipped full AI automation — it was accurate, but users focused on what was wrong rather than what was right. Onboarding stalled. Working with the team, we introduced deterministic logic first, then layered AI on top. That gave users a fast, reliable baseline before surfacing AI refinements. It shifted the experience from "prove the AI is correct" to "here's a starting point you can build confidence in."

"That sequencing decision — not the AI itself — was what unlocked adoption."

Migration sequenced like a product launch

The migration off the legacy platform was treated as a product problem, not an ops handover. The first cohort was ~$200k ARR — selected for low data complexity and low migration friction. The goal was to use it to build the playbook for the higher-risk accounts, not to move fast.

Solving the adoption problem

The biggest risk wasn't the system. It was whether compliance SMEs — whose professional identity was tied to doing this work manually — would actually use it. That's an identity problem, not a usability problem. The response: prioritise the business outcome experience for P&L owners, give SMEs transparency and control — not to prove accuracy, but to let them maintain their expert role within an automated system.

"They weren't resistant because the AI was wrong. They were resistant because automation challenged their expertise."

IMPACT

Net-new revenue, protected at-risk ARR, and onboarding cut from weeks to minutes

  • ~$500k net new ARR from zero (Sustainability vertical)

  • 9 new logos, 7 upsells/retention saves, 4 renewals in first phase

  • $3.76M at-risk ARR in active protection motion

  • Onboarding time: weeks (solution engineer-led) → minutes (self-serve)

  • Sales cycle: ~5-month enterprise cycle → self-serve / faster conversion

  • V1 → V2 shipped, ~30-customer beta, positioned for scale

WHAT THIS TAUGHT ME

Correct isn't enough — trust is the product, and adoption is the real metric

Correct AI outputs aren't enough. Users need to understand why before they'll act. Building trust is a product strategy problem, not an engineering problem.

And adoption is the product. The system working is a necessary condition, not a sufficient one. The question is always: will the right people actually use this in their daily workflow?